Decision Trees for Event Signature Classification on Fiber Optic Cables in Quaternion Coordinates

Proximal events posing risks to network service were classified using Decision Trees on State of Polarization Multivariate Time Series data. Aggregate features of interests were individually evaluated to determine their significance, demonstrating that a combination of two aggregates sufficed to produced 98.8% event classification accuracy.